Abstract:  Aiming at the problem of pedestrian multi-attributes recognition under surveillance video, this paper proposes a multi-classification method combining neural network and association rules. Firstly, the attributes confidence of pedestrian in surveillance video can be obtained through Faster-RCNN detection algorithm and improved AlexNet multi-classification network. Then, it adopts Apriori association rules to deal with the training data. After combining neural network classification confidence and the results of association rules, it proposes an algorithm to optimize classification confidence. Finally, by analyzing the accuracy rate of some pedestrian attributes optimized by association rules, the results show that the effective combination of neural network and association rules can improve the accuracy of some attributes recognition.

Key words: pedestrian attribute, multi-classification, neural network, association rules, optimization